Kernel Principal Component Analysis and Multidimensional Scaling

video-placeholder
Loading...
View Syllabus

Skills You'll Learn

Dimensionality Reduction, Unsupervised Learning, Cluster Analysis, K Means Clustering, Principal Component Analysis (PCA)

Reviews

4.7 (160 ratings)

  • 5 stars
    79.37%
  • 4 stars
    14.37%
  • 3 stars
    2.50%
  • 2 stars
    1.87%
  • 1 star
    1.87%

AF

Nov 6, 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

MK

Feb 21, 2022

Thank you Coursera.

Thank you IBM.

Thank you to all instructors.

From the lesson

Nonlinear and Distance-Based Dimensionality Reduction

This module introduces dimensionality reduction techniques like Kernal Principal Component Analysis and multidimensional scaling. These methods are more powerful than Principal Component Analysis in many applications.

Taught By

  • Placeholder

    Mark J Grover

    Digital Content Delivery Lead

  • Placeholder

    Miguel Maldonado

    Machine Learning Curriculum Developer

  • Placeholder

    Joseph Santarcangelo

    Ph.D., Data Scientist at IBM

  • Placeholder

    Xintong Li

    Data Scientist at IBM

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.